Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

Destination images of non-visitors

This article provides much needed understanding of destination images held by non-visitors. Recognizing the characteristics of non-visitor images and their formation is important in order to understand images more widely. This qualitative study assesses images of London. The views of three hundred people in the Czech Republic who have never visited London were obtained via an innovative open-ended research instrument. The study showed that non-visitors imagine destinations through comparisons with their own experiences of places. Findings indicate that images can be very persistent and that the first images formed of a destination endure over time. Although the research is based on people with no direct experience of London, the research highlights that a range of secondary ‘experiences’ influence image formation.